A Proposed Model for Loan Approval Prediction Using Explainable Artificial Intelligence

With the rapid growth in the banking sector due to the current inflation in Egypt, many people go to the bank for a loan. This paper proposes a model that explains the result of the loan approval of bank applicants using explainable artificial intelligence. this study uses the "Give Me Some Credit" dataset with more than 120k rows so the paper trains the model on earlier records of bank applicants who applied before for a bank loan. The paper aims to elucidate the significance of employing explainable artificial intelligence in the banking industry, with a particular focus on loan approval. it shows how it is beneficial to use XAI instead of using manual loan approval or even using normal algorithms to approve or decline bank loaners. The paper clarifies the machine learning algorithm's explanation, disregarding model accuracy in favor of focusing solely on explaining the model to bankers. This involves replacing the traditional machine learning algorithm as the decision maker with the banker, who can approve or deny loans using the model's explanation.